What does this failure look like in production?

In production this failure typically appears during the first tens-to-hundreds of training steps: loss curves become non-monotonic, validation metrics plateau above the baseline, and downstream checkpoints either diverge or fail to improve. Engineers frequently misread it as data drift when the underlying mechanism is python references holding gpu tensors past their useful life. Validating against the symptom list in this entry is the fastest way to confirm the diagnosis.

Is this a hardware or a software issue?

Memory failures can look like either layer depending on the symptom. The discriminator is the resolution of the symptom to the recommended fix: if applying the steps under "How to fix it" eliminates the symptom, this is a software/configuration class issue regardless of the hardware it surfaced on. Repeat the fix on a fresh node to confirm.

How long does the fix typically take?

Most resolutions from this entry take 10 to 30 minutes of engineer time once the diagnosis is confirmed. Long fixes come from skipping the diagnosis step and chasing apparent symptoms (validation drift, memory growth, slow throughput) instead of the actual root cause listed above. Apply the fix in a small experiment first, then roll it forward.

Does this affect inference or only training?

Primarily this is a memory failure observed during training, but the same misconfiguration can leak into downstream evaluation and serving. Treat the prevention checklist as a guardrail: any pipeline that touches a trained checkpoint should re-validate the failure mode before promoting the model.

What's the cheapest way to confirm this is the problem?

Cheapest signal: reproduce the symptom on a 1-GPU toy run with the same configuration. If the toy run exhibits the same behavior, the cause is the configuration rather than the cluster. If only the multi-GPU run is affected, the failure is a distributed interaction, so Long-running training jobs is the most likely layer to inspect.

Can the Denpex platform detect this automatically?

Yes. Denpex correlates the symptoms in this entry against your training logs in real time and surfaces the failure taxonomy, root cause, and exact remediation command on the console. Three full diagnoses are included free during signup, and the encyclopedia entry you are reading is generated from the same signal library that powers the platform.

What related failures should I check next?

Closely related failures cluster around #cuda, #memory, #leak. After applying this fix, validate against the related entries below to confirm no overlapping cause is masking as this one. Engineers routinely discover that the dominant failure mode is actually a second, sibling failure compounded on top.

Will this recur after the fix?

Applied once with the prevention checklist, this failure does not recur under normal operation. The recurring variant most engineers encounter is the re-emergence of the same symptom on a different model or stack after cuda is upgraded. Pin compatible versions and rerun the prevention smoke test on every dependency change.

How does this scale from 1 GPU to 1000 GPUs?

At single-GPU scale the symptom usually looks like slow convergence or a single anomalous loss value. At 64+ GPU scale, distributed interactions amplify python references holding gpu tensors past their useful life through collectives, and the failure can manifest as a full job abort. Always reproduce the fix at the smallest cluster configuration that still exhibits the symptom before promoting to production scale.

Is there a fingerprint I can monitor?

Yes. Nvidia-smi shows reserved memory growing across iterations is the highest-signal fingerprint. Wire a metric or alert on that signal (or its absence after the fix) so you can detect the recurrence automatically. The Denpex platform encodes this exact fingerprint and surfaces it directly in the dashboard.

What is the official documentation that covers this?

Reference documentation is listed in the References section at the bottom of this entry. The most authoritative URLs are the upstream framework docs and the issue trackers cited; the Denpex entry synthesizes both into the executive, engineer, and on-call perspectives so each audience can act without reading every linked source.

Does this entry include working code?

Yes. The "Code Examples" section contains copy-paste-ready snippets that target the most common stacks (PyTorch, Hugging Face Trainer, DeepSpeed, Megatron-LM, vLLM/Transformers). Each snippet is annotated with why it works so you can adapt the pattern to your own framework without losing fidelity.

CUDA Memory Leak

CUDA memory leaks accumulate over training, eventually causing OOM. Denpex tracks allocation patterns to identify leaks.

Quick answer

CUDA memory leaks accumulate over training, eventually causing OOM.

Symptom: nvidia-smi shows reserved memory growing across iterations
Root cause: Python references holding GPU tensors past their useful life. PyTorch caching allocator fragmentation. Custom CUDA operations with manual memory.
Recommended fix: Use del to remove references to large tensors
How Denpex helps: Denpex investigates CUDA Memory Leak using the evidence you provide or your connected workload collects. Earlier rank, host or application evidence is needed to distinguish an initiating failure from a downstream report.

Memory#cuda#memory#leak#allocation#pytorch#oom

What this failure is

CUDA Memory Leak is a Memory failure seen during ML training runs. CUDA memory leaks accumulate over training, eventually causing OOM. Denpex tracks allocation patterns to identify leaks. Common tags: Cuda, Memory, Leak, Allocation.

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Why it happens (the mechanism)

Python references holding GPU tensors past their useful life. PyTorch caching allocator fragmentation. Custom CUDA operations with manual memory. Hidden allocations in third-party libraries. Taken together, these mechanisms explain why the failure is reproducible, why it tends to surface on specific workloads or scales, and why generic mitigation attempts often fall short without addressing the underlying cause.

What you'll observe

GPU memory usage increases linearly across training steps
Memory not released after model deletion
OOM errors after hours of training that didn't occur at start

Common symptoms and what they mean

Symptom	Why it happens
nvidia-smi shows reserved memory growing across iterations	Python references holding GPU tensors past their useful life
torch.cuda.memory_allocated() monotonically increases	PyTorch caching allocator fragmentation
No way to free memory without restart	Custom CUDA operations with manual memory

Which systems are affected

Long-running training jobs
Models with complex forward passes
Custom training loops with manual tensor management

How to confirm this is the problem

Use this checklist to test the hypothesis against a small reproduction. No single line proves the root cause, so preserve the preceding events and compare one variable at a time.

✓Reproduce the failure from a clean checkpoint/seed: the symptom must appear without warm-up state from a previous run.
✓Verified signal present: nvidia-smi shows reserved memory growing across iterations
✓Verified signal present: torch.cuda.memory_allocated() monotonically increases
✓Verified signal present: No way to free memory without restart
✓A targeted fix from the "How to fix it" section eliminates or substantially reduces the symptom within one validation pass.

Root cause

Python references holding GPU tensors past their useful life
PyTorch caching allocator fragmentation
Custom CUDA operations with manual memory
Hidden allocations in third-party libraries

The fix and how to prevent it

Searchable error signature

search key

nvidia-smi shows reserved memory growing across iterations
torch.cuda.memory_allocated() monotonically increases

Use this text as a lookup key in logs and upstream issue trackers. It is not presented as a captured customer log. Confirm the cause from your own preceding events, versions, configuration and the cited references.

The fix and the prevention pattern

The root cause is on this page and stays free. A free account adds the exact remediation steps, saved history, and the fix on every entry in the encyclopedia.

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Diagnose this failure in VS Code

Select the traceback or open the failed terminal, then run Denpex locally to see the initiating rank, collateral failures, exact fix, and verification command without uploading the log.

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CUDA errors in context

CUDA reports errors asynchronously, so the traceback usually points at whatever line synchronised next rather than the one at fault. The hub covers every common CUDA error and how to make it report honestly.

Compare every cuda error side by side